Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning

  Objective  To establish an artificial intelligence-assisted diagnosis system for molecular subtyping of colorectal cancer (CRC).

  Methods  812 whole-slide images (WSIs) of 422 patients were selected from the database of The Cancer Genome Atlas (TCGA) and were put into the training set (75%) and the test set (25%). The slides were stored in the www.paiwsit.com database. We preprocessed and segmented the slides based on the labelling results of experienced pathologists to generate a training set of more than 4 million labeled samples. Finally, deep learning models were adopted for training.

  Results  After training with several convolutional neural network models, we tested the performance of the trained deep learning model on the test set of 203 WSIs from 110 patients, and our model achieved an accuracy of 53.04% at patch-level and 51.72% at slide-level, while the accuracy of CMS2 (one of a consensus of four subtypes for CRC) at slide-level was as high as 75.00%.

  Conclusion  This study is of great significance to the promotion of colorectal cancer screening and precision treatment.